print "working on example", datum_id
if data_iter.next(
compute_pattern_times=True,
max_template_length=classifier.window[1]):
pattern_times = data_iter.pattern_times
num_detections = data_iter.E.shape[1] - classifier.window[1]
num_frames += data_iter.E.shape[1]
scores = -np.inf * np.ones(num_detections)
for d in xrange(num_detections):
E_segment = data_iter.E[:,d:d+classifier.window[1]].copy()
esp.threshold_edgemap(E_segment,.30,edge_feature_row_breaks,report_level=False,abst_threshold=abst_threshold)
esp.spread_edgemap(E_segment,edge_feature_row_breaks,edge_orientations,spread_length=3)
if classifier_flat.score_no_bg(E_segment[:,:classifier_flat.window[1]]) > flat_threshold:
scores[d] = classifier.score_no_bg(E_segment)
# now we get the indices sorted
indices = cl.remove_overlapping_examples(np.argsort(scores)[::-1],
classifier.window[1],
int(allowed_overlap*classifier.window[1]))
positives, negatives = cl.get_pos_neg_scores(indices,pattern_times,
scores,classifier.window[1])
all_positives.extend(positives)
all_negatives.extend(negatives)
else:
break
aar_def_roc,aar_def_roc_vals = cl.get_roc(np.sort(all_positives)[::-1],
np.sort(all_negatives)[::-1],num_frames)
np.save('aar_def_roc_vals052112',aar_def_roc_vals)
coarse_count_scores[d] and\
coarse_count_scores[d-1]>\
coarse_count_scores[d-2]) ):
coarse_scores[d] = classifier.score_no_bg(E_segment)
# get the positive and negative scores removed out
pos_counts =[]
pos_scores = []
neg_indices = np.empty(scores.shape[0],dtype=bool)
neg_indices[:]=True
for pt in xrange(len(pattern_times)):
pos_scores.append(np.max(scores[pattern_times[pt][0]-int(np.ceil(classifier.window[1]/3.)):pattern_times[pt][0]+int(np.ceil(classifier.window[1]/3.))]))
pos_counts.append(np.max(coarse_scores[pattern_times[pt][0]-int(np.ceil(classifier.window[1]/3.)):pattern_times[pt][0]+int(np.ceil(classifier.window[1]/3.))]))
neg_indices[pattern_times[pt][0]-int(np.ceil(classifier.window[1]/3.)):pattern_times[pt][1]] = False
# get rid of overlapping instances
neg_indices_counts_non_overlap = cl.remove_overlapping_examples(np.argsort(coarse_scores),
classifier.coarse_length,
int(allowed_overlap*classifier.coarse_length))
neg_indices_non_overlap = cl.remove_overlapping_examples(np.argsort(scores),
classifier.window[1],
int(allowed_overlap*classifier.window[1]))
neg_idx2 = np.empty(scores.shape[0],dtype=bool)
neg_idx2[neg_indices_non_overlap] =True
neg_indices_full = np.logical_and(neg_indices,neg_idx2)
neg_idx2 = np.empty(scores.shape[0],dtype=bool)
neg_idx2[neg_indices_counts_non_overlap] =True
neg_indices_coarse = np.logical_and(neg_indices,neg_idx2)
all_positive_scores.extend(pos_scores)
all_positive_counts.extend(pos_counts)
all_negative_scores.extend(scores[neg_indices_full])
all_negative_counts.extend(coarse_scores[neg_indices_coarse])
else:
coarse_scores = -np.inf * np.ones(num_detections)
bg = mean_background.copy()
for d in xrange(num_detections):
E_segment = data_iter.E[:,d:d+classifier.window[1]].copy()
esp.threshold_edgemap(E_segment,.30,edge_feature_row_breaks,report_level=False,abst_threshold=abst_threshold)
esp.spread_edgemap(E_segment,edge_feature_row_breaks,edge_orientations,spread_length=3)
scores[d] = classifier.score_no_bg(E_segment)
scores_adapt_bg[d] = classifier.score(E_segment,bg)
coarse_scores[d] = classifier.coarse_score_count(E_segment)
bg = np.minimum(.1,
np.maximum(np.mean(E_segment,axis=1),
.4))
# now we get the indices sorted
indices = cl.remove_overlapping_examples(np.argsort(scores)[::-1],
classifier.window[1],
int(allowed_overlap*classifier.window[1]))
indices_adapt_bg = cl.remove_overlapping_examples(np.argsort(scores_adapt_bg)[::-1],
classifier.window[1],
int(allowed_overlap*classifier.window[1]))
indices_coarse = cl.remove_overlapping_examples(np.argsort(coarse_scores)[::-1],
classifier.window[1],
int(allowed_overlap*classifier.window[1]))
positives, negatives = cl.get_pos_neg_scores(indices,pattern_times,
scores,classifier.window[1])
positives_adapt_bg, negatives_adapt_bg = cl.get_pos_neg_scores(indices_adapt_bg,
pattern_times,
scores_adapt_bg,
classifier.window[1])
positives_coarse, negatives_coarse = cl.get_pos_neg_scores(indices_coarse,
pattern_times,
